I use tokenized europarl v7 corpus for training, and tokenized WMT News news-test2013 for evaluation. For time saving, I use only the first 10 sentences in this evaluation dataset. The configuration is at the end of this post.

At the beginning (of course, after a few checkpoints, say at ckpt-5000), the BLEU score looked fine (it increased, and had been at about 9.0). But after one night, at ckpt-70000, the BLEU score became 0.

With a relative small dataset (2M sentences), is this situation expected? Or there is something wrong in my setting?

The following are target sentences and predictions at about ckpt-5000 and ckpt-70000

target: A Republican strategy to counter the re-election of Obama
ckpt-5000: An interim strategy for combating <unk> is <unk> .
ckpt-70000: This is an important issue .
target: Unlike in Canada , the American States are responsible for the organisation of federal elections in the United States .
ckpt-5000: On the contrary , the American States are responsible for elections in the United States .
ckpt-70000: The European Union ’ s security policy is a good example of the European Union .
target: Republican leaders justified their policy by the need to combat electoral fraud .
ckpt-5000: The political leaders of their political leaders have to fight against fraud .
ckpt-70000: The vote will take place tomorrow at 12 noon .

And the following are the 10 predictions at ~ckpt0-70000, which seems strange:

This is an important issue .
The vote will take place tomorrow at 12 noon .
It is also important that the European Union has a duty to do so .
I would like to thank the rapporteur for his excellent work .
It is a matter of urgency .
It is also important for the European Union to play a leading role in this area .
The European Union ’ s security policy is a good example of the European Union .
It is also important to ensure that the European Union ’ s security policy .
The Commission ’ s proposal for a directive on the implementation of the Lisbon Strategy .
The European Union ’ s security strategy is a key issue .

# The directory where models and summaries will be saved. It is created if it does not exist.
model_dir: Model/TransformerBig/
data:
# (required for train_and_eval and train run types).
train_features_file: Data/Training/Tokenized/europarl-v7.fr-en.fr_tokenized
train_labels_file: Data/Training/Tokenized/europarl-v7.fr-en.en_tokenized
# (required for train_end_eval and eval run types).
eval_features_file: Data/Evaluation/Tokenized/newstest-2013.fr_tokenized.txt
eval_labels_file: Data/Evaluation/Tokenized/newstest-2013.en_tokenized.txt
# (optional) Models may require additional resource files (e.g. vocabularies).
source_words_vocabulary: fr-vocab-30000-tokenized.txt
target_words_vocabulary: en-vocab-30000-tokenized.txt
# source_tokenizer_config: config-tokenization.yml
# target_tokenizer_config: config-tokenization.yml
params:
gradients_accum: 1
# Training options.
train:
batch_size: 3000
# (optional) Batch size is the number of "examples" or "tokens" (default: "examples").
batch_type: tokens
# (optional) Save a checkpoint every this many steps.
save_checkpoints_steps: 100
# (optional) How many checkpoints to keep on disk.
keep_checkpoint_max: 10
# (optional) Save summaries every this many steps.
save_summary_steps: 100
# (optional) Train for this many steps. If not set, train forever.
train_steps: 100000
# (optional) The number of threads to use for processing data in parallel (default: 4).
num_threads: 4
# (optional) The number of elements from which to sample during shuffling (default: 500000).
# Set 0 or null to disable shuffling, -1 to match the number of training examples.
sample_buffer_size: 0
# (optional) Number of checkpoints to average at the end of the training to the directory
# model_dir/avg (default: 0).
average_last_checkpoints: 0
# (optional) Evaluation options.
eval:
# (optional) The batch size to use (default: 32).
batch_size: 10
# (optional) The number of threads to use for processing data in parallel (default: 1).
num_threads: 4
# (optional) Evaluate every this many seconds (default: 18000).
eval_delay: 0
# (optional) Save evaluation predictions in model_dir/eval/.
save_eval_predictions: True
# (optional) Evalutator or list of evaluators that are called on the saved evaluation predictions.
# Available evaluators: BLEU, BLEU-detok, ROUGE
external_evaluators: [BLEU]
# (optional) Model exporter(s) to use during the training and evaluation loop:
# last, final, best, or null (default: last).
exporters: last

OK, because I had issues about DataLossError before, and you suggested me to give it a small value, and I tried even with 0 and the issue was still there. Now, it seems I don’t have DataLossError issue anymore, but I forgot to restore the parameters.

After changing sample_buffer_size to the default value 500000, the BLEU still went to 0. At the beginning, it was better than sample_buffer_size = 0, I even saw BLEU > 25 at some checkpoints. But it finally went to 0 and never went back.

I tried with “Transformer” model instead of “TransformerBig” with the 2M sentences europarl dataset, and it worked. At the 100000 steps, the BLEU score is about 23 on the full WMT News news-test2013 dataset.

So maybe this relative dataset is not enough to train a TransformerBig. Or, my batch_size=3000 is too small to make TransformerBig well trained? I see people saying TransformerBig is sensitive to batch_size.